A major challenge in modern machine learning applications is balancing statistical efficiency with the privacy of individuals from whom data is obtained. In such applications, privacy is often quantified in terms of Differential Privacy (DP) [1]}. DP has several variants, including approximate DP [2]}, Rényi DP [3]}, and others [4]}, [5]}, [6]}, [7]}. Arguably, the most stringent flavor of DP is local differential privacy (LDP) [8]}, [9]}. Intuitively, a randomized mechanism (or a Markov kernel) is said to be locally differentially private if its output does not vary significantly with arbitrary perturbations of the input.
